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Operationalizing the Use of Sensor Data in Mobile Crowdsensing: A Systematic Review and Practical Guidelines

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2023)

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Abstract

Smartphones have found their way into many domains because they can be used to measure phenomena of common interest. The Global Overview Report Digital 2022 states that two-thirds of the world’s population uses a smartphone. This creates a power for measurements that many researchers would like to leverage. However, this in turn requires standardized approaches to collaborative data collection. Mobile crowdsensing (MCS) is a paradigm that pursues collaborative measurements with smartphones and the available sensor technology. Although literature on MCS has existed since 2006, there is still little work that has systematically studied existing systems. Especially when developing technical systems based on MCS, design decisions must be made that affect the subsequent operation. In this paper, we therefore conducted a PRISMA-based literature review on MCS, considering two aspects: First, we wanted to be able to better categorize existing systems, and second, we wanted to derive guidelines for developers that can support design decisions. Out of a total of 661 identified publications, we were able to include 117 papers in the analysis. Based on five main criteria (application area, goals, sensor utilization, time constraints, processing device), we show which goals the research area is currently pursuing and which approaches are being used to achieve these goals. Following this, we derive practical guidelines to support researchers and developers in making design decisions.

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Authors and Affiliations

  1. Institute of Databases and Information Systems, Ulm University, Ulm, Germany

    Robin Kraft, Maximilian Blasi, Marc Schickler & Manfred Reichert

  2. Department of Clinical Psychology and Psychotherapy, Ulm University, Ulm, Germany

    Robin Kraft

  3. Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany

    Rüdiger Pryss

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  1. Robin Kraft
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  2. Maximilian Blasi
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  5. Rüdiger Pryss
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Correspondence to Rüdiger Pryss .

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  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool, Suzhou, China

    Xinheng Wang

  3. University of Peloponnese, Patra, Greece

    Nikolaos Voros

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Kraft, R., Blasi, M., Schickler, M., Reichert, M., Pryss, R. (2024). Operationalizing the Use of Sensor Data in Mobile Crowdsensing: A Systematic Review and Practical Guidelines. In: Gao, H., Wang, X., Voros, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 563. Springer, Cham. https://doi.org/10.1007/978-3-031-54531-3_13

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